*For some time now, our regular contributor James Glover been promising me a post with some statistical analysis of historical global temperatures. To many the science of climate change seems inaccessible and the “debate” about climate change can appear to come down to whether you believe a very large group of scientists or a much smaller group of scientists people. Now, with some help from James and a beer coaster, you can form your own view.*

How I wish that the title of this article was literally true and not just a play on words relating to the Harmonic Series. Sadly, the naysayers are unlikely to be swayed, but read this post and you too can disprove global warming denialism on the back of a beer coaster!

It is true, I have been promising the Mule a statistical analysis of Global Warming. Not only did I go back and look at the original temperature data but I even downloaded the data and recreated the original “hockey stick” graph. For most people the maths is quite complicated though no more than an undergraduate in statistics would understand. It all works out. As a sort of professional statistician, who believes in Global Warming and Climate Change, I can only reiterate my personal mantra: there is no joy in being found to be right on global warming.

But before I get onto the beer coaster let me give a very simple explanation for global warming and why the rise in CO_{2} causes it. Suppose I take two sealed glass boxes. They are identical apart from the fact that one has a higher concentration of CO_{2}. I place them in my garden (let’s call them “greenhouses”) and measure their temperature, under identical conditions of weather and sunshine, over a year. Then the one with more CO_{2} will have a higher temperature than the one with less. Every day. Why? Well it’s simple: while CO_{2} is, to us, an “odourless, colourless gas” this is only true in the visible light spectrum. In the infra-red spectrum, the one with more CO_{2} will be darker. This means it absorbs more infrared radiation and hence has a higher temperature. CO_{2} is invisible to visible light but, on it’s own, would appear black to infrared radiation. The same phenomenon explains why black car will heat up more in the sun than a white one. This is basic physics and thermodynamics that was understood in the 19th century when it was discovered that “heat” and “light” were part of the same phenomenon, i.e. electromagnetic radiation.

So why is global warming controversial? Well, while what I said is undeniably true in a pair of simple glass boxes, the earth is more complicated than these boxes. Radiation does not just pass through, it is absorbed, reflected and re-radiated. Still, if it absorbs more radiation than it receives then the temperature will increase. It is not so much the surface temperature itself which causes a problem, but the additional energy that is retained in the climate system. Average global temperatures are just a simple way of trying to measure the overall energy change in the system.

If I covered the glass box containing more CO_{2} with enough aluminium foil, much of the sunshine would be reflected and it would have a lower temperature than its lower CO_{2} twin. Something similar happens in the atmosphere. Increasing temperature leads to more water vapour and more clouds. Clouds reflect sunshine and hence there is less radiation to be absorbed by the lower atmosphere and oceans. It’s called a negative feedback system. Maybe that’s enough to prevent global warming? Maybe, clouds are very difficult to model in climate models, and water vapour is itself a greenhouse gas. Increasing temperature also decreases ice at the poles. Less ice (observed) leads to less radiation reflected and more energy absorbed. A positive feedback. It would require a very fine tuning though for the radiation reflected back by increased clouds to exactly counteract the increased absorption of energy due to higher CO_{2}. Possible, but unlikely. Recent models show that CO_{2} wins out in the end. As I as said, there is no joy to being found right on global warming.

So enough of all that. Make up your own mind. Almost time for the Harmony. Perusing the comments of a recent article on the alleged (and not actually real) “pause” in global warming I came across a comment to the effect that “if you measure enough temperature and rainfall records then somewhere there is bound to be a new record each year”. I am surprised they didn’t invoke the “Law of Large Numbers” which this sort of argument usually does. Actually The Law of Large Numbers is something entirely different, but whatever. So I asked myself, beer coaster and quill at hand, what is the probability that the latest temperature or rainfall is the highest since 1880, or any other year for that matter?

Firstly, you can’t prove anything using statistics. I can toss a coin 100 times and get 100 heads and it doesn’t prove it isn’t a fair coin. Basically we cannot know all the possible set ups for this experiment. Maybe it is a fair coin but a clever laser device adjusts its trajectory each time so it always lands on heads. Maybe aliens are freezing time and reversing the coin if it shows up tails so I only think it landed heads. Can you assign probabilities to these possibilities? I can’t.

All I can do is start with a hypothesis that the coin is fair (equal chance of heads or tails) and ask what is the probability that, despite this, I observed 100 heads in a row. The answer is not zero! It is actually about 10^{-30}. That’s 1 over a big number: 1 followed by 30 zeros. I am pretty sure, but not certain, that it is not a fair coin. But maybe I don’t need to be certain. I might want to put a bet on the next toss being a head. So I pick a small number, say 1%, and say if I think the chance of 100 head is less than 1% then I will put on the bet on the next toss being heads. After 100 tosses the hypothetical probability (if it was a fair coin) is much less than my go-make-a-bet threshold of 1%. I decide to put on the bet. It may then transpire that the aliens watching me bet and controlling the coin, decide to teach me a lesson in statistical hubris and make the next toss tails and I lose. Unlikely, but possible. Statistics doesn’t *prove* anything. In statistical parlance the “fair coin” hypothesis is called the “Null Hypothesis” and the go-make-a-bet threshold of 1% is called the “Confidence Level”.

Harmony. Almost. What is the probability that if I had a time series (of say global temperature since 1880) that the latest temperature is a new record. For example the average temperature in Australia in 2013 was a new record. The last average global temperature record was in 1998. I think it is trending upwards over time with some randomness attached. But there are all sort of random process which produce trends, some of which are equally likely to have produced a downward trending temperature graph. All I can really do, statistically speaking, is come up with a Null Hypothesis. In this case my Null Hypothesis is that the temperature doesn’t have a trend but is just the result of random chance. There are various technical measures to analyse this, but I have come up with one you can fit on the back of a beer coaster.

So my question is this: if the temperature readings are just i.i.d. random processes (i.i.d. stands for “independent and identically distributed”) and I have taken 134 of these (global temperature measurements 1880-2014) what is the probability the latest one is the maximum of them all? It turns out to be surprisingly easy to answer. If I have 134 random numbers then one of them must be the maximum. Obviously. Since they are iid I have no reason to believe it will be the first, second, third,…, or 134th. It is equally likely to be any one of those 134. So the probability that the 134th is the maximum is 1/134 = 0.75% (as it is equally likely that, say, the 42nd is the maximum). If I have T measurements then the probability that the latest is the maximum is 1/T. So when you hear that the latest global temperature is a maximum, and you don’t believe in global warming, then be surprised. As a corollary if someone says there hasn’t been a new maximum since 1998 then the probability of this still being true, 14 years later, is 1/14 = 7%.

So how many record years do we expect to have seen since 1880? Easy. Just add up the probability of the maximum (up to that point) having occurred in each year since 1880. So that would be H(T) = 1 + 1/2 + 1/3 + … + 1/T. This is known as the Harmonic Series. It is famous in mathematics because it almost, but doesn’t quite converge. For our purposes it can be well approximated by H(T) =0.5772+ ln(T) where ln is the natural logarithm, and 0.5772 is known as the Euler-Mascharoni constant.

So for T=134 we get from this simple beer-coaster sized formula: H(134) = 0.5772+ln(134)= 5.47. (You can calculate this by typing “0.5772+ln(134)” into your Google search box if you don’t have a scientific calculator to hand). In beer coaster terms 5.47 is approximately 6. So, given the Null Hypothesis (which is that there has been no statistically significant upward trend since 1880) how many record breaking years do we expect to have seen? Answer: less than 6. How many have we seen: 22.

**Global temperatures* – labelled with successive peaks**

If I was a betting man I would bet on global warming. But there will be no joy in being proven right.

*James rightly points out that the figure of 22 peak temperatures is well above the 6 you would expect to see under the Null Hypothesis. But just how unlikely is that high number? And, what would the numbers look like if we took a different Null Hypothesis such as a random walk? That will be the topic of another post, coming soon to the Stubborn Mule!*

* The global temperature “anomaly” represents the difference between observed temperatures and the average annual temperature between 1971 and 2000. Source: the National Climate Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA).